Article

$67,895 vs $2.9 Million: The Real Cost of Building a Software Portfolio with AI vs Traditional Teams

AI Economics

Key Takeaways
  • Building a production software portfolio has historically required one thing above all else: capital.
  • Glassdoor and Levels.fyi compensation data for 2024-2025 puts the fully loaded cost of a mid-level US software engineer at $150,000-$220,000 annually, including benefits, equity, and overhead.
  • The cost differential is not the result of finding cheaper labor.
  • The $67,895 vs $2.9 million comparison is not a theoretical exercise.

Published: February 2026 | Stealth Labz | Search Intent: Commercial Investigation Keywords: AI development cost vs traditional, software portfolio build cost, AI vs dev team cost comparison

The Setup

Building a production software portfolio has historically required one thing above all else: capital. Lots of it. The standard formula -- hire a development team, staff a project manager, add QA, budget for DevOps -- produces cost structures that start in the high six figures and scale into the millions for anything beyond a single product. Accelerance's 2024 global outsourcing rate analysis shows blended developer rates ranging from $50/hour in Southeast Asia to $250/hour for senior US-based engineers. A mid-market US team of 4-6 developers plus support staff runs $80,000-$120,000 per month. A 12-month build therefore costs $960,000-$1,440,000 before a single customer sees the product.

The conventional approach to managing this cost is to narrow scope. Build one product. Ship it. Generate revenue. Then reinvest to build the next. Each new product restarts the cycle: new budget, new timeline, new risk. This is why multi-product portfolios are the province of well-funded companies. Building ten production systems in the traditional model means ten separate budgets, ten project timelines, and a cumulative spend that compounds in the wrong direction. Clutch.co's 2024 custom software development survey confirms the range: median custom platform builds run $100,000-$500,000. Complex multi-integration systems push well past $750,000.

The problem is not that these numbers are unreasonable. Developers cost money. Good ones cost more. The problem is that the cost structure assumes every project starts from zero -- every new product requires a full team, a full build cycle, and a full budget. The compounding works against you. What if it could work for you instead?

What the Data Shows

External Benchmarks

Glassdoor and Levels.fyi compensation data for 2024-2025 puts the fully loaded cost of a mid-level US software engineer at $150,000-$220,000 annually, including benefits, equity, and overhead. A standard build team -- four engineers, one project manager, one QA engineer -- runs $960,000-$1,440,000 for a 12-month engagement. Agencies charge more: Clutch.co reports that US-based custom development firms bill $150-$300/hour for full-stack work, pushing a complex platform build to $780,000-$1,560,000 at the low end. Accelerance's global benchmarks show that even fully offshore teams (Eastern Europe, Latin America) run $50-$100/hour, meaning a 12-month portfolio build still lands between $400,000-$800,000 when offshored entirely.

For a portfolio of 10 production systems spanning multiple verticals and geographies, the market-rate estimate is $795,000-$2,900,000 at standard US mid-market pricing. That range accounts for an operations platform ($780K-$1,560K), an insurance quoting cluster of four products ($320K-$640K), seasonal e-commerce ($120K-$240K), dual-geography insurance quoting ($120K-$240K), a reporting platform ($60K-$120K), and a legal services platform ($45K-$90K). Timeline: 12-24 months. Team: 4-6 developers plus PM plus QA.

Internal Evidence

The CEM portfolio -- 10 production systems, 596,903 lines of custom code, 2,561 commits across 10 repositories -- was built for $67,895. That is the total. Contractors accounted for $65,054 (CON-02 at $40,700, CON-03 at $21,854, and an initial website build at $2,500). AI tools and software accounted for $2,841 (Anthropic Claude, OpenAI, and minor software). Timeline: 4 months of active build. Team: 1 operator plus AI assistance.

The math produces a cost multiple of 12x-43x. The same portfolio that would have cost $795,000 at the low end of market rates was built for $67,895 -- 8.5 cents on the dollar at best case, 2.3 cents on the dollar at worst case.

Where the Money Did Not Go

The traditional cost model includes roles that the CEM approach eliminated entirely. A technical architect at $20,000/month over 12 months: $0. A QA engineer at $10,000/month over 12 months: $0. A project manager at $12,000/month over 12 months: $0. A DevOps engineer at $8,000/month over 12 months: $0. Combined traditional team overhead for 12 months: $960,000-$1,440,000 -- none of which was spent.

No investors. No venture capital. No equity sold. 100% retained ownership across every product.

How It Works

The cost differential is not the result of finding cheaper labor. The primary contractor (CON-02) charged standard market rates. The cost collapsed because of how the work compounded.

Under CEM (Compounding Execution Model), every project produces two outputs: the product itself and reusable infrastructure -- authentication patterns, database schemas, admin interfaces, API structures, deployment pipelines, error handling templates. The first project requires building all of this from scratch. The second project inherits most of it. By the ninth product, 95%+ of infrastructure came from the existing foundation.

The per-project cost trajectory tells the story: Project 1 cost $7,995 in external support. Project 4 cost $1,680. Project 8 cost $90. Project 9 cost $0. The marginal cost of a new production system reached zero -- not through negotiation or corner-cutting, but because the accumulated infrastructure meant there was almost nothing left to build that was new.

Monthly burn rate followed the same curve. September 2025: $8,367. October: $6,070. November: $6,999. December: $1,035. January 2026: $825. A 90% reduction in 4 months. The operator did not shrink the scope. The system made each subsequent build cheaper because the infrastructure compounded.

What This Means for Decision-Makers Evaluating AI Development Costs

The $67,895 vs $2.9 million comparison is not a theoretical exercise. It is a QB-verified, git-audited record of what 10 production systems actually cost versus what they would cost to rebuild at standard market rates today.

For any organization evaluating build-versus-hire decisions, the implication is structural: the cost of software development is no longer primarily a function of team size or hourly rates. It is a function of how effectively each build contributes to the next. An operator working under a compounding methodology with AI-assisted development produced output that would have required a full team at 12x-43x the cost. The quality held -- a 12.1% product defect rate against an industry norm of 20-50%. The timeline compressed from what would have been 12-24 months to 4 months.

The question is not whether AI reduces development costs. The data shows it does, dramatically. The question is whether your cost structure is built for compounding -- or whether every new project still starts from zero.

Related: How the Marginal Cost of New Software Approaches Zero with AI Development | 620x Cost Reduction: How a Solo AI Developer Matches a Dev Shop's Output

References

  1. Accelerance (2024). "Global Software Outsourcing Report." Outsourced development market rates by country.
  2. Glassdoor/Levels.fyi (2024–2025). Software developer compensation data.
  3. Clutch.co (2024). "Custom Software Development Survey." Agency pricing and project cost data.
  4. Keating, M.G. (2026). "Case Study: The Cost Inversion." Stealth Labz. Read case study